Practical Deep Learning with PyTorch

Learn how to build and deploy Deep Neural Networks to solve common Machine Learning problems

Instructor: Joe Papa, O'Reilly Author & Trainer
Updated: 2022

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Live 2-Day Course
Instructor-led Hands-on Training (Virtual)
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Live 2-Day Course

Hands-on, Instructor-led Training (Virtual)

July 21-22, 2022

Register Now

What you will learn:

This two-day course will teach you how to design, train, and deploy Deep Neural Networks using Python and PyTorch. Your course starts with learning how to represent data with PyTorch tensors. You’ll then develop ML models using DNN to solve problems like image classification and text analysis using CNN and other types of NN architectures. This course is hands-on and you’ll be executing code in Google Colab on both CPU and GPU processors.

During this course, you will:

  • Understand how Deep Neural Networks work
  • Apply Machine Learning methods to create Deep Learning models
  • Discover popular existing DL models and how to leverage them for your own purpose
  • Review DL architectures like CNN, GAN, RNN, and Transformers and when to use them
  • Discover the common problems in Deep Learning and how to overcome them
  • Understand why you should use Batch Normalization, Dropout, or different Activation functions
  • Learn how to solve problems like:
    • Image Classification
    • Object Detection
    • NLP Sentiment Analysis
  • Learn how to apply DL methods to your own work
    Become a PyTorch expert in designing, training, and deploying DL models

This course is for you, if:

You’re a student, developer, data scientist, researcher, or engineer interested in learning how to use Deep Learning and AI to solve problems.  You’re eager to build your skills in one of the most popular DL frameworks, PyTorch, and you want to understand how you can apply Machine Learning to your own work. In addition, you may also want to complete side projects using Deep Learning to build your experience, advance your career or land a new job in AI.

Course Contents

Day 1

Course Overview
Review Machine Learning Process & Python for Machine Learning
PyTorch Tensors
Coding Exercise: Tensors
PyTorch Data
Coding Exercise: Custom Datasets
Solving ML problems with existing NN
Coding Exercise: Image Classification

Day 2

Deep Learning Architectures
Common DL Problems
Designing Deep Networks
Coding Exercise: Designing a CNN
Training NN
Coding Exercise: Training NN for Text Analysis
Deploying NN
Advanced Topics

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Practical Deep Learning with PyTorch

Build and deploy models to solve common ML problems

Video Poster Image

Live 2-Day Course

Instructor-led Hands-on Training (Virtual)

Register Now

Certificate of Completion

Print a copy for your wall or post your certificate on social media sites like LinkedIn.

1.5 CEUs

Earn 15 hours of training towards your continuing education goals. 

Workbook Included

You'll receive a complete workbook for class participation and future reference.

Post-Course Training

We'll continue to send you optional coding challenges after the course so you can continue to practice. 

Meet Your Instructor

Joe Papa has helped over 6,000 students build their skills in Deep Learning and AI.  He holds an MSEE, 25+ years experience in R&D, and has led AI Teams at Booz Allen, Perspecta Labs, and Mobile Insights. Joe has published two books on PyTorch and Deep Learning with O'Reilly and has published multiple online courses with Udemy and Packt Publishing.

Matt Kirk, Chief Data Scientist

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Dyllan McCreary, Sr. AI Engineer

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Axel Sarota, AI Researcher

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Pay Full

$1,499

Corporate Pricing

  • 2 Full Days of Advanced Training
  • Coding Exercises
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Payment Plan

6 x $249

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Live 2-Day Course (Virtual)

Instructor-led, Hands-on Training

July 21-22, 2022

Register